Auto Tag: A Collaborative Approach to Automated Tag Assignment for Weblog Posts Gilad Mishne ISLA, University of Amsterdam Kruislaan 403. 1098SJ Amsterdam. The Netherlands gilad@science. uva. nl ABSTRACT posts will be tagged in the first place, and second by offer- This paper describes AutoTag tags ing relevant tags that may have not been applied otherwise. for weblog posts using collaborat This in turn improves the tasks for which tagging is aimed evaluation of AutoTag on a large hows at, providing better search and browse capabilities good accuracy; coupled with the 二 coIl- trol, AutoTag assists both in simplifying the tagging process 2. TAG ASSIGNMENT and in improving its quality. Our basic approach to automated tag assignment is that of collaborative filtering, or recommender systems Categories and Subject descriptors nutshell, a recommender system helps users find desirable products or services by analyzing their profile and matching H.3.4 Information Systems]: Systems and Software it with profiles of other users similar to them, or by finding General terms products that are similar to the ones they expressed inter- est in: it is assumed that similar users share similar tastes Human Factors, Languages Amazon, TiVo, Netflix and others are among the many suc- Keywords cessful applications of commercial recommender systems. An application of collaborative filtering methods to au tomated tag discovery becomes clear when the"user"and INTRODUCTION "product concepts are examined from a different angle. In AutoTag, the blog posts themselves take the role of users Tagging is an old-new method for organizing data by as- and the tags assigned to them function as the products that signing descriptors to documents and other sources of infor- the users expressed interest in. In traditional recommender mation. These descriptors, or"tags", are typically short tex- systems, similar users are assumed to buy similar products tual labels, which provide an easy way to categorize, search AutoTag makes the same assumption, and identifies use- and browse the information they describe. The annotation ful tags for a post by examining tags assigned to similar of documents with keywords is nothing new by itself, but a posts. Just as with traditional reco ems. the collaborative form of this method with some ur erties is attracting a lot of attention on the web in recent recommendations are then further improved by incorporat ing external knowledge about the bloggers, the posts, or the ears. The main characteristics of collaborative tagging dif- tags ferentiating it from traditional key word annotation are its open-vocabulary, non-hierarchical nature, and the fact that Write blog entry Find similar posts ags are assigned by authors and users of the information Weblog post rather than by professional annotators, with no rules or lim- itations [1, 2. Tagging is particularly popular in some web Similar Weblog Posts mediums such as photo sharing websites(e. g, Flickr)and the blogosphere, where tags are often assigned to weblog This paper addresses the task of automatic assignment of tags to weblog posts; while some work on weblog classifica- tion exists, we are not aware of published work about tag scovery. To this end, we describe a system-AutoTag- that, given a weblog post, offers a small number of tags Figure 1: Flow of information in AutoTag hich seem useful for it; the blogger then reviews the sug- The different stages of the tag suggestion process in Au- gestions, selecting those which she finds instrumental. More toTag are shown in Figure 1. Once the user supplies a we- than just simplifying the tagging process, Auto Tag also im- blog post, posts which are similar to it are identified. Next proves its quality: first, by increasing the chance that weblog the tags assigned to these posts are aggregated, creating a ranked list of likely tags. In the next stage, AutoTag filters and reranks this tag list; finally, the top-ranked tags are of- 2006, May 22-26, 2006, Edinburgh, Scotland fered to the user, who selects the tags to attach to the post 1-59593-332-906/0005 We follow with additional details about each step
AutoTag: A Collaborative Approach to Automated Tag Assignment for Weblog Posts Gilad Mishne ISLA, University of Amsterdam Kruislaan 403, 1098SJ Amsterdam, The Netherlands gilad@science.uva.nl ABSTRACT This paper describes AutoTag, a tool which suggests tags for weblog posts using collaborative filtering methods. An evaluation of AutoTag on a large collection of posts shows good accuracy; coupled with the blogger’s final quality control, AutoTag assists both in simplifying the tagging process and in improving its quality. Categories and Subject Descriptors H.3.4 [Information Systems]: Systems and Software General Terms Human Factors, Languages Keywords Blogs, tags 1. INTRODUCTION Tagging is an old-new method for organizing data by assigning descriptors to documents and other sources of information. These descriptors, or “tags”, are typically short textual labels, which provide an easy way to categorize, search, and browse the information they describe. The annotation of documents with keywords is nothing new by itself, but a collaborative form of this method with some unique properties is attracting a lot of attention on the web in recent years. The main characteristics of collaborative tagging differentiating it from traditional keyword annotation are its open-vocabulary, non-hierarchical nature, and the fact that tags are assigned by authors and users of the information rather than by professional annotators, with no rules or limitations [1, 2]. Tagging is particularly popular in some web mediums such as photo sharing websites (e.g., Flickr) and the blogosphere, where tags are often assigned to weblog posts to facilitate categorization and filtering. This paper addresses the task of automatic assignment of tags to weblog posts; while some work on weblog classification exists, we are not aware of published work about tag discovery. To this end, we describe a system—AutoTag— that, given a weblog post, offers a small number of tags which seem useful for it; the blogger then reviews the suggestions, selecting those which she finds instrumental. More than just simplifying the tagging process, AutoTag also improves its quality: first, by increasing the chance that weblog Copyright is held by the author/owner. WWW 2006, May 22–26, 2006, Edinburgh, Scotland. ACM 1-59593-332-9/06/0005. posts will be tagged in the first place, and second by offering relevant tags that may have not been applied otherwise. This in turn improves the tasks for which tagging is aimed at, providing better search and browse capabilities. 2. TAG ASSIGNMENT Our basic approach to automated tag assignment is that of collaborative filtering, or recommender systems [3]. In a nutshell, a recommender system helps users find desirable products or services by analyzing their profile and matching it with profiles of other users similar to them, or by finding products that are similar to the ones they expressed interest in; it is assumed that similar users share similar tastes. Amazon, TiVo, Netflix and others are among the many successful applications of commercial recommender systems. An application of collaborative filtering methods to automated tag discovery becomes clear when the “user” and “product” concepts are examined from a different angle. In AutoTag, the blog posts themselves take the role of users, and the tags assigned to them function as the products that the users expressed interest in. In traditional recommender systems, similar users are assumed to buy similar products; AutoTag makes the same assumption, and identifies useful tags for a post by examining tags assigned to similar posts. Just as with traditional recommender systems, the recommendations are then further improved by incorporating external knowledge about the bloggers, the posts, or the tags. Similar Weblog Posts Weblog Post Tag1 Tag2 Tag3 Tag4 ... Tag3 Tag1 Tag4 Write blog entry Find similar posts Filter and rerank Aggregate tags Figure 1: Flow of information in AutoTag The different stages of the tag suggestion process in AutoTag are shown in Figure 1. Once the user supplies a weblog post, posts which are similar to it are identified. Next, the tags assigned to these posts are aggregated, creating a ranked list of likely tags. In the next stage, AutoTag filters and reranks this tag list; finally, the top-ranked tags are offered to the user, who selects the tags to attach to the post. We follow with additional details about each step
Finding Similar Posts. Auto Tag uses Information Retrieval only the first 10 results are checked because it is assumed measures to estimate the similarity between weblog posts that users are unwilling to examine long result lists. For the n practice, this means a large collection of posts is indexed automated evaluation, we measured recall at 10 as well: the by an IR engine, and a query generated from the original fraction of tags offered by Auto Tag in the top-10 suggestions post is submitted to this engine. The most similar posts are which were also assigned by the blogger out of the total hen taken to be the highest-ranking ones retrieved from the collection, using some retrieval model We experimented with a number of methods for generat. t Evaluation Precision @10 Recall@1o ing queries from a post, including using the entire text of 0.59 the post and using links in it to locate cocitations. The best Manual osts Automated 0.40 results were obtained by using a" distinctive term"query standard corpus comparison methods are first used to de rive the most distinctive terms in the vocabulary of a post Table 1: Auto-tagging accuracy compared to the general vocabulary in the corpus:the Results are shown in Table 1; an example of tags offered by top-ranking terms are in turn used as the query. Auto Tag is given in Table 2. On average, 4 to 6 suggestion out of AutoTag's top-10 suggestions are either considered A Tag Model. Auto Tag uses simple heuristics for compos- useful by the blogger, or were actually used by her for the ing the ranked list of tags from the top-retrieved posts: each given post. Cursory examination of posts for which Auto Tag tag is scored according to its frequency in the top results. scores low shows many non-English posts(for whic Experimenting with more complex ways of scoring the tags, less data exists in the corpus, entailing lower success taking into account the retrieval rank or score, yielded only driven methods), and many tags which are highly minor improvements in accuracy. Filtering and Reranking. One clear source of informa- and used by few bloggers(such as names of family members) ion we have about a blogger is the tags she already used http://www prior to writing the post being analyzed. Therefore, if such previously-used tags appear in the ranked list, AutoTag boosts their score by a constant factor. nments on how to pitch a product to him as a blogger, and suggestions as Lindsay agrees getting distracted 3.EⅤ ALUATION how this For evaluating our method, we used the corpus distributed by Intelliseek for the 3rd Weblogging Workshop. The cor- pus contains 10M weblog posts collected during a period of 3 weeks; of these, 1.8M posts are tagged, with a total of 2. 3M uggested tags: PR, blogging, weblogs, marketing, net, gripes, tags. For indexing and retrieval, we used the open source nail, small business life, Anil Dash, PR pitching ngine Lucene which uses a rather simple vector space re- trieval model; text was stemmed with an English stemmer Two methods were used to evaluate the effectiveness of the Table 2: Sample tags suggested for a post tag suggestion methods. First, we manually examined the 4. CONCLUSIONS lection; for each tag, we decided whether the tag was indee e proposed and evaluated AutoTag, a tool for tagging a relevant label for the post. This is the preferred method weblog posts based on a collaborative filtering approach. of evaluation, but due to its cost it can only be applied to Auto Tag offers suggestions for tags based on tags assign to other, similar posts; the final decision about a tag is left a small number of posts; additionally, it is difficult to use to the blogger. Despite a relatively small corpus for this type non automated methods to tune and im a system. Be- of task, Auto Tag shows good results, and has the potential ause of this, we used an automated method to evaluate a much larger subset of posts: AutoTag was used to tag 6000 to benefit both the bloggers and others making use of tags f the " tagged posts"in our corpus- the posts which were assigned to weblog posts. assigned tags by their authors (we used only posts with 3 The different components of Auto Tag provide fertile ground or more tags ). Then, AutoTag's output was compared to for further work: identifying effective ways to generate queries he actual post tags. To account for minor differences in from a post and successful retrieval models to use; improving tags ("blogs"and"blogging"), we used string distance to the aggregation of tags from the retrieved posts; and vari- compare the tags rather than exact string match. Even so, by Auto Tag. In addition to the collaborative approaches since tags which are useful for a post but were not originally cal"approach to tag suggestion, in which suggestions for sed by its author are mistakenly taken to be incorrect. To demonstrate this. we evaluated the small test set with the tags are made without access to the entire blogosphere as automated method as well, resulting in substantially lower is the case with AutoTag, but using deeper analysis of the res: this indicates that the actual performance of Auto- contents of the post and the blog it belongs to. Tag on the large set is likely to be much better than reported 5. REFERENCES by the automated evaluation 1S. Golder and B. A H an. The structure of collaborative For the manual evaluation, we measured precision at 10 the fraction of tags out of the top-10 suggestions by AutoTag [2 A. Mathes Folksonomies: Cooperative classification and which were judged as appropriate for the post by a human Communication. 2004. [3] P Resnick and H. R. Varian. Recommender systems(spe http://www.blogpulse.com/www2006-workshop section). Comm. of the ACM, 40(3), 1997
Finding Similar Posts. AutoTag uses Information Retrieval measures to estimate the similarity between weblog posts. In practice, this means a large collection of posts is indexed by an IR engine, and a query generated from the original post is submitted to this engine. The most similar posts are then taken to be the highest-ranking ones retrieved from the collection, using some retrieval model. We experimented with a number of methods for generating queries from a post, including using the entire text of the post and using links in it to locate cocitations. The best results were obtained by using a “distinctive term” query: standard corpus comparison methods are first used to derive the most distinctive terms in the vocabulary of a post (compared to the general vocabulary in the corpus); the top-ranking terms are in turn used as the query. A Tag Model. AutoTag uses simple heuristics for composing the ranked list of tags from the top-retrieved posts: each tag is scored according to its frequency in the top results. Experimenting with more complex ways of scoring the tags, taking into account the retrieval rank or score, yielded only minor improvements in accuracy. Filtering and Reranking. One clear source of information we have about a blogger is the tags she already used prior to writing the post being analyzed. Therefore, if such previously-used tags appear in the ranked list, AutoTag boosts their score by a constant factor. 3. EVALUATION For evaluating our method, we used the corpus distributed by Intelliseek for the 3rd Weblogging Workshop.1 The corpus contains 10M weblog posts collected during a period of 3 weeks; of these, 1.8M posts are tagged, with a total of 2.3M tags. For indexing and retrieval, we used the open source engine Lucene which uses a rather simple vector space retrieval model; text was stemmed with an English stemmer. Two methods were used to evaluate the effectiveness of the tag suggestion methods. First, we manually examined the tags assigned to a random subset of 30 posts from our collection; for each tag, we decided whether the tag was indeed a relevant label for the post. This is the preferred method of evaluation, but due to its cost it can only be applied to a small number of posts; additionally, it is difficult to use non automated methods to tune and improve a system. Because of this, we used an automated method to evaluate a much larger subset of posts: AutoTag was used to tag 6000 of the “tagged posts” in our corpus - the posts which were assigned tags by their authors (we used only posts with 3 or more tags). Then, AutoTag’s output was compared to the actual post tags. To account for minor differences in tags (“blogs” and “blogging”), we used string distance to compare the tags rather than exact string match. Even so, the automated precision scores are lower than manual ones, since tags which are useful for a post but were not originally used by its author are mistakenly taken to be incorrect. To demonstrate this, we evaluated the small test set with the automated method as well, resulting in substantially lower scores; this indicates that the actual performance of AutoTag on the large set is likely to be much better than reported by the automated evaluation. For the manual evaluation, we measured precision at 10: the fraction of tags out of the top-10 suggestions by AutoTag which were judged as appropriate for the post by a human; 1http://www.blogpulse.com/www2006-workshop only the first 10 results are checked because it is assumed that users are unwilling to examine long result lists. For the automated evaluation, we measured recall at 10 as well: the fraction of tags offered by AutoTag in the top-10 suggestions which were also assigned by the blogger out of the total number of tags assigned by her. Test Set Evaluation Precision@10 Recall@10 30 posts Automated 0.38 0.47 30 posts Manual 0.59 — 6000 posts Automated 0.40 0.49 Table 1: Auto-tagging accuracy Results are shown in Table 1; an example of tags offered by AutoTag is given in Table 2. On average, 4 to 6 suggestions out of AutoTag’s top-10 suggestions are either considered useful by the blogger, or were actually used by her for the given post. Cursory examination of posts for which AutoTag scores low shows many non-English posts (for which much less data exists in the corpus, entailing lower success of datadriven methods), and many tags which are highly personal and used by few bloggers (such as names of family members). http://www.stillhq.com/diary/000959.html On pitching products to bloggers Anil comments on how to pitch a product to him as a blogger, and makes good suggestions as Lindsay agrees before getting distracted by how this applies to press releases. I have to wonder though how much of this promotional pitching actually happens. I certainly haven’t ever had a product pitched to me for this site. I’ve had people pitch advertising, and other spammy things, but not products. Does it really happen to us normal people bloggers? - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Suggested tags: PR, blogging, weblogs, marketing, net, gripes, email, small business life, Anil Dash, PR pitching Original tags: blog, pitch, product, marketing, communications Table 2: Sample tags suggested for a post 4. CONCLUSIONS We proposed and evaluated AutoTag, a tool for tagging weblog posts based on a collaborative filtering approach. AutoTag offers suggestions for tags based on tags assigned to other, similar posts; the final decision about a tag is left to the blogger. Despite a relatively small corpus for this type of task, AutoTag shows good results, and has the potential to benefit both the bloggers and others making use of tags assigned to weblog posts. The different components of AutoTag provide fertile ground for further work: identifying effective ways to generate queries from a post and successful retrieval models to use; improving the aggregation of tags from the retrieved posts; and various methods for filtering and reranking the lists produced by AutoTag. In addition to the collaborative approaches described in this paper, we are currently investigating a “local” approach to tag suggestion, in which suggestions for tags are made without access to the entire blogosphere as is the case with AutoTag, but using deeper analysis of the contents of the post and the blog it belongs to. 5. REFERENCES [1] S. Golder and B. A. Huberman. The structure of collaborative tagging systems. J. Inf. Science, 2006. [2] A. Mathes. Folksonomies: Cooperative classification and communication through shared metadata. Computer Mediated Communication, 2004. [3] P. Resnick and H. R. Varian. Recommender systems (special section). Comm. of the ACM, 40(3), 1997